PAKT: A Position-Aware Self-attentive Approach for Knowledge Tracing

Knowledge Tracing aims to model a student’s knowledge state from her past learning interactions and predict her performance in future. Although structures such as positional encoding or forgetting gate have already been used in Knowledge Tracing models, positional information with great potential is not fully utilized. In this paper, we propose a Position-aware Self-Attentive Knowledge Tracing (PAKT) model with a position supervision mechanism. Massive experimental results show that PAKT outperforms other benchmarks on several popular datasets.